Triple
T1195620
| Position | Surface form | Disambiguated ID | Type / Status |
|---|---|---|---|
| Subject | Toledo |
E25661
|
entity |
| Predicate | nickname |
P55
|
FINISHED |
| Object |
Glass City
Glass City is a nickname for Toledo, Ohio, reflecting its historic prominence in the glass manufacturing industry.
|
E139116
|
NE FINISHED |
How this triple was built (4 steps)
Every LLM step that produced this triple, in pipeline order — named-entity classification, the disambiguation choices (the exact options shown, with the pick highlighted), and the generated description. The batch + timestamp of each is in the Provenance table below.
NER
Named-entity recognition
gpt-5-mini
Instruction
Given a phrase, classify it is english named entity (e.g., persons, organizations, works of art) in Latin script, or not (e.g., literals, dates, URLs, verbose phrases). For disambiguation, the statement where the phrase occurs as object is also given. Please return a JSON object with `phrase` (string, the phrase being analyzed) and `is_ne` (boolean, indicating whether the phrase is a Named Entity).
Input
Phrase: Glass City | Statement: [Toledo, nickname, Glass City]
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: Glass City Context triple: [Toledo, nickname, Glass City]
-
A.
River City
River City is a popular nickname for Sacramento, California, highlighting the city’s close connection to the nearby American and Sacramento Rivers.
-
B.
River City
River City is a popular nickname for Wuhan, a major central Chinese metropolis known for its location at the confluence of the Yangtze and Han rivers.
-
C.
River City
River City is a popular nickname for Richmond, Virginia, highlighting the city's location along the James River and its historic riverfront character.
-
D.
Spindle City
Spindle City is a historic industrial nickname for Lowell, Massachusetts, reflecting its prominence as a major 19th-century textile manufacturing center.
-
E.
Steeltown
Steeltown is a nickname for the Canadian city of Hamilton, reflecting its historic prominence as a major steel-producing industrial center.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NEDg
Description generation
gpt-5.1
Instruction
Generate a one-sentence description of the target entity. You are given a context triple in the form (subject, predicate, object), where the object is the target entity. # Instructions Use the triple to infer relevant information about the entity. Describe the entity based on what is most defining, well-known. Avoid repeating the information from the triple, unless really essential. # Response Format Return only the sentence: "Description: [one-sentence description of the target entity]"
Input
Entity: Glass City Triple: [Toledo, nickname, Glass City]
Generated description
Glass City is a nickname for Toledo, Ohio, reflecting its historic prominence in the glass manufacturing industry.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: Glass City Target entity description: Glass City is a nickname for Toledo, Ohio, reflecting its historic prominence in the glass manufacturing industry.
-
A.
River City
River City is a popular nickname for Wuhan, a major central Chinese metropolis known for its location at the confluence of the Yangtze and Han rivers.
-
B.
River City
River City is a popular nickname for Sacramento, California, highlighting the city’s close connection to the nearby American and Sacramento Rivers.
-
C.
River City
River City is a popular nickname for Richmond, Virginia, highlighting the city's location along the James River and its historic riverfront character.
-
D.
Spindle City
Spindle City is a historic industrial nickname for Lowell, Massachusetts, reflecting its prominence as a major 19th-century textile manufacturing center.
-
E.
Steeltown
Steeltown is a nickname for the Canadian city of Hamilton, reflecting its historic prominence as a major steel-producing industrial center.
- F. None of above. chosen
Provenance (5 batches)
The batch behind each pipeline step, in order, with when it ran. Timestamps are batch-level — stages were processed in waves, so the object chain (NER → NED1 → NEDg → NED2) reads in order, but predicate / elicitation batches can sit in a different wave.
| Step | Stage | Batch ID | Status | When |
|---|---|---|---|---|
| creating | Elicitation | batch_69a49429f5ec8190a6a205eb0ae81e5e |
completed | March 1, 2026, 7:31 p.m. |
| NER | Named-entity recognition | batch_69a4bd7a756c819085d695acfffeaceb |
completed | March 1, 2026, 10:28 p.m. |
| NED1 | Entity disambiguation (via context triple) | batch_69ac83146f2881909e230bc9de28a76b |
completed | March 7, 2026, 7:57 p.m. |
| NEDg | Description generation | batch_69ac837e06cc8190b0da34646fa78c0c |
completed | March 7, 2026, 7:58 p.m. |
| NED2 | Entity disambiguation (via description) | batch_69ac84309acc8190aac6c3c78246b352 |
completed | March 7, 2026, 8:01 p.m. |
Created at: March 1, 2026, 7:46 p.m.